Scaling properties of pyramidal neurons in mice neocortex

Abstract

Dendritic morphology is the structural correlate for receiving and processing inputs to a neuron. An interesting question then is what the design principles and the functional consequences of enlarged or shrinked dendritic trees might be. As yet, only a few studies have examined the effects of neuron size changes. Two theoretical scaling modes have been analyzed, conservative (isoelectrotonic) scaling (preserves the passive and active response properties) and isometric scaling (steps up low pass-filtering of inputs). It has been suggested that both scaling modes were verified in neuroanatomical studies. To overcome obvious limitations of these studies like small size of analyzed samples and restricted validity of utilized scaling measures, we considered the scaling problem of neurons on the basis of large sample data and by employing a more general method of scaling analysis. This method consists in computing the morphoelectrotonic transform (MET) of neurons. The MET maps the neuron from anatomical space into electrotonic space using the logarithm of voltage attenuation as the distance metric. The theory underlying this approach is described and then applied to two samples of morphologically reconstructed pyramidal neurons (cells from neocortex of wildtype and synRas transgenic mice) using the NEURON simulator. In a previous study, we could verify a striking increase of dendritic tree size in synRas pyramidal neurons. Surprisingly, in this study the statistical analysis of the sample MET dendrograms revealed that the electrotonic architecture of these neurons scaled roughly in a MET-conserving mode. In conclusion, our results suggest only a minor impact of the Ras protein on dendritic electroanatomy, with non-significant changes of most regions of the corresponding METs.

Mechanisms of Functional Hypoconnectivity in the Medial Prefrontal Cortex ofMecp2Null Mice

“…The reduction in E/I balance of synaptic currents in Mecp2 null neurons could directly influence cortical UP-state dynamics, because UP-state termination is controlled by the strength of inhibition relative to excitation (Fanselow and Connors 2010). Excitatory UP-states could also be influenced directly by changes in NMDA currents (Milojkovic et al 2005;Favero and Castro-Alamancos 2013) as well as the spatial organization of synapses on cortical dendrites, which can influence the duration of UP-state synaptic activity by controlling the relative timing or synchrony of synaptic inputs (Schierwagen et al 2007;Sceniak and Sabo 2010;Wang et al 2010;Plotkin et al 2011).…”

Somato-dendritic morphology and dendritic signal transfer properties differentiate between fore- and hindlimb innervating motoneurons in the frog Rana esculenta

“…In the METs the distance between any dendritic point and the soma scales with the electrotonic distance of the two points, permitting a visual approach to passive signal transfer. This qualitative approach was first taken by Zador et al [35] for different neurons of the brain and rarely followed by quantitative analysis of the METs (but see [76,95]). Here we went further and analyzed METs of MNs quantitatively for the first time and compared MNs responsible for moving the fore- and hindlimbs of frogs.…”

Mathematical and Computational Modeling of Neurons and Neuronal Ensembles

“…In the study [4], we employed compartmental modeling to perform a comparative electrotonic analysis of two samples of cortical pyramidal neurons, one from wildtype, and the other from transgenic mice. While anatomical dendritic trees of transgenic pyramidal neurons were significantly enlarged, the statistical analysis of the sample morphoelectrotonic (MET) dendrograms revealed that the transgenic neurons scaled in a MET-conserving mode.…”

“…This is due to the experimentally available techniques which can be used to demonstrate correlations between recorded activity patterns and behavior of the animal 4 . Because of the nature of the modeling relation [17] for complex systems like the brain, an ultimate proof that a particular computation produces a particular behavior is not possible.…”